利用規範
Acceptable Use Policy v2.0Version 2.0 · · Governance 2.0 public evidence surface
Governance 2.0 Overview
This page is part of the starnum public Governance 2.0 surface and uses the same evidence layer as the system card, data governance, transparency report, use policy, and security policy.
Governance Summary
This page sets the practical rules for using the platform without treating outputs as professional advice or guaranteed predictions.
Scope
Allowed learning use, prohibited high-stakes reliance, harmful-use boundaries, affiliate disclosure, and shared footer governance links.
Implementation Status
Version 2.0 makes the policy testable through risk-boundary audits and link-safety verification.
本規範は、starnum.com.tw プラットフォーム上の占術コンテンツ（紫微斗数、数秘術）および口語解説サービスの合理的な利用範囲を定めます。本サイトを利用する時点で、本規範への同意とみなされます。
一、占術コンテンツの位置づけ
本サイトのすべての占術コンテンツは文化的な自己探求ツールであり、性格傾向・エネルギー特性・人生テーマの参考視点を提供するものです。以下のものではありません：
- 医療診断、治療助言、代替療法
- 法律助言、金融投資助言
- 心理的治療やカウンセリング
- 科学的に検証可能な予測システム
二、許容される利用範囲
- 個人の性格探求と自己理解
- 人間関係の理解（家族・友人・仕事仲間）
- キャリア傾向や才能探求の補助的参考
- 文化研究・学術引用（出典を明記してください）
- 娯楽目的の占術リーディング
- 有資格のカウンセラーと併用する補助ツール
- 教育目的（伝統的な東洋術数文化の学習）
三、禁止される利用
3.1 絶対禁止（プラットフォームが即時削除またはブロック措置を講じます）
- 犯罪予測：占術情報を用いて他者の犯罪傾向や人格を予測・断定すること
- 差別的利用：命盤や数秘に基づき、雇用・住居・融資などで個人を差別すること
- 他者の操作：占術解読で恐怖・不安・心理的操作を行うこと
- 名誉毀損・攻撃：実在の個人（公人を含む）の命盤を解読し攻撃手段とすること
- 未成年者：保護者の同意なく 13 歳未満の児童に個人占術分析を提供すること
- 迷信的脅迫：「必ず凶事が起きる」「直ちに運命を改めよ」などの脅迫的表現を用いること
- 詐欺目的：本サイトのコンテンツを金銭詐欺、霊的治療販売、虚偽の主張に利用すること
3.2 特に注意が必要（利用者の責任）
- 人生の重大な決断（結婚・投資・手術）で占術結果に過度に依存すること
- 占術解読を理由に専門的な医療支援を拒否すること
- 本人の同意なく他者の命盤情報を拡散すること
四、AI 支援生成コンテンツの利用規範
本サイトの一部の口語解説は AI 支援により生成され、人間によるレビューを経ています。AI 支援による占術コンテンツを引用・共有する際は、以下に注意してください：
- 占術文化の参考であり、事実的言明ではないことを明確に表示する
- 人間の占術師の個人的助言になりすます目的で使用しない
- 引用時は原典（starnum.com.tw）を明記することを推奨する
五、執行措置
本サイトは、本規範に違反する利用者に対し以下の措置を講じる権利を留保します：
- 違反コンテンツの削除
- サービス利用の制限または停止
- 必要に応じて法的調査への協力
六、規範の更新
本規範はサービスの発展に伴い更新される場合があります。重要な変更は @mychenan にて告知し、ページ冒頭のバージョン日付を更新します。
七、お問い合わせ
本規範についてご質問がある場合は、Instagram @mychenan までご連絡ください。
外部基準と一次資料
以下は本ページの判断に用いる一次資料です。比較基準であり、第三者による本サイトの推奨を意味しません。
Current Machine Audit Snapshot
This block uses only traceable local audit data. No unsupported metrics or model claims are added.
- data/state-machine/i18n-parity.json: 8,036 parent URLs, 7,976 articles.
- data/kb-machine-audit.json: 3,238 source files, 0 missing coverage, 0 orphan chunks.
- data/discovery-surface-audit.json: 0 errors, 0 warnings.
- data/sla-report.json: critical / 5 critical, 0 warnings.
Content Maintenance And Update Decision
This block makes governance-page content machine-checkable: every page must disclose its source artifacts, related pages, and the gate that reports update needs.
Update Decision
This is not static copy. When source artifacts, related policies, public metrics, or generators change, AI Ops reports evidence and an AI agent decides whether the page needs edits.
Human Boundary
Systems detect, report, and preserve machine-readable evidence. Codex/Claude agents perform final judgment and repair.
Verification Command
node scripts/verify-trust-pages.js --check
data/public-claim-registry.jsondata/ai-answer-readiness-audit.jsondata/state-machine/trust-pages.json- Related governance pages: Privacy Policy · Ethics Statement · AI Safety · FAQ
- Update flow:
npm run update:trust-pages→npm run test:trust
Verifiable Evidence Layer
This block is not a narrative claim. Each core assertion has a claim id, source JSON, hash, and a repeatable verification command. Public pages disclose governance evidence without exposing source code, secrets, private data, or exploitable attack details.
| Claim ID | Verifiable value | Status | Owner | Source and verification |
|---|---|---|---|---|
| claim.public-url-manifest.indexable-count Public URL and canonical inventory |
38,965 indexable URLs | verified | sitewide | node scripts/generate-public-evidence-manifest.js --dry |
| claim.trust-pages.audit-pass-rate Trust page machine audit |
180/180 pass | verified | sitewide | node scripts/verify-trust-pages.js --check |
| claim.discovery-surface.zero-errors AI discovery surface audit |
{"errors":0,"warnings":0} | verified | sitewide | node scripts/verify-discovery-surface.js |
| claim.structured-data.jsonld-errors JSON-LD / structured data audit |
{"structured_data_invalid_files":0,"breadcrumb_count":28274,"faq_count":27506,"dataset_count":30,"article_count":27406} | verified | sitewide | node scripts/site-machine-audit.js |
| claim.status.sla-state Status page SLA source |
critical / 5 critical, 0 warnings | verified | sitewide | node scripts/generate-status-page.js |
| claim.provider-alignment.openai-anthropic-gemini OpenAI / Anthropic / Google Gemini benchmark alignment |
benchmark alignment only unless code/config evidence exists | verified | sitewide | node scripts/verify-public-evidence.js --check |
| claim.transparency-report.sha256 Transparency report SHA-256 anchor |
{"report":"transparency/report-2026-Q3.json","sha256":"47b09e2ca4e8b8fe9dffdfaccef3b11212de9ee3a8a14badca8044e2481203c5"} | verified | sitewide | node scripts/update-transparency-current-data.js |
| claim.release-integrity.gpg-signing GPG signing status |
GPG signing configured locally; GitHub verification pending | github_verification_pending | sitewide | gpg --list-secret-keys --keyid-format=long && git log -1 --show-signature |
| claim.acceptable-use.risk-boundary-coverage Acceptable-use risk-boundary coverage |
{"articles":7976,"aiAnswerReady":7976,"riskBoundaryArticles":2239,"failures":0} | verified | acceptable-use | node scripts/verify-public-evidence.js --check |
| claim.acceptable-use.provider-use-boundary Provider alignment is not a production model claim |
{"unsupportedProductionModelClaimsBlocked":true,"rule":"Named frontier models are not treated as production usage unless code/config evidence exists.","benchmarkProviders":["OpenAI | verified | acceptable-use | node scripts/verify-public-evidence.js --check |
| claim.acceptable-use.footer-link-safety Footer link safety audit |
{"pages":180,"pass":180,"fail":0} | verified | acceptable-use | node scripts/verify-trust-pages.js --check |
System Card V2.0: Technical Transparency Layer
This layer publishes the technical governance evidence that can be safely disclosed: architecture, data sources, AI-use boundaries, quality gates, release integrity, and provider alignment. Source code, secrets, exploitable attack details, and private data remain out of scope.
Public architecture
Cloudflare Pages/Workers, R2/D1/KV/Pagefind, and local generation scripts form the public-site and governance publication chain. Public pages disclose behavior, state, and traceable sources, not secrets or internal permissions.
AI-use disclosure
AI-assisted workflows are used for knowledge-base retrieval, cross-checking, and error detection. Governance documents are benchmarked against OpenAI, Anthropic, and Google Gemini public frameworks. Production model usage is disclosed only when code/config evidence exists.
Quality and safety gates
Governance page audit 180/180 passing, JSON-LD errors 0, discovery-surface errors 0. Status pages report critical / 5 critical, 0 warnings as-is.
Data traceability
Knowledge base 32,724 chunks, TM 789,031 entries, AI answer-ready 7,976/7,976. Public metrics trace to data/state-machine/*, data/*audit*.json, and transparency reports.
| Governance area | OpenAI | Anthropic | Google Gemini | Starnum implementation evidence |
|---|---|---|---|---|
| Model/system-card disclosure | OpenAI models + safety docs | Claude model docs + system/model cards | Gemini model docs + safety settings | system-card, model-card, methodology, benchmark, transparency-log |
| Safety evaluation and use boundaries | Safety best practices / deployment checklist | Responsible Scaling / safety policy | Gemini safety controls / policy | AI safety, acceptable-use, ethics, risk-boundary copy, crawler policy audit |
| Data governance | Data controls / privacy controls | privacy and data handling docs | Gemini API data governance references | privacy, ai-data-governance, KB/TM source tracking, SHA-256 hashes |
| Monitoring and release | production checklist / eval discipline | system-card transparency discipline | model/version documentation discipline | deploy.js, status.html, SLA report, trust-pages-machine-audit, sitemap/hreflang audits |
- Sources: data/state-machine/model-card.json, public-bench.json, trust-pages.json, security-headers.json.
- Sources: data/trust-pages-machine-audit.json, data/discovery-surface-audit.json, data/ai-answer-readiness-audit.json.
- Sources: data/kb-machine-audit.json, data/tm/quality-audit-report.json, data/sla-report.json.
- Official benchmark docs checked: 2026-07-30; links are listed in the OpenAI / Anthropic / Google Gemini alignment table.
The V2.0 goal is not more claims; it separates implemented controls from planned controls. Production usage, benchmark alignment, status exceptions, GPG signing, and SLA breaches are disclosed from source data.
Release Integrity And GPG
GPG signing configured locally. signingkey=0934DFA0EDA6363A. GitHub verification pending until the public key upload and Verified badge are confirmed.
OpenAI / Anthropic / Google Gemini Alignment
The governance surface is benchmarked against the three public frameworks: model docs, system/model cards, safety evaluation, data governance, and use policies. This is benchmark alignment, not a claim that every provider is active in production inference. Official docs checked: 2026-07-30
| Provider | Governance focus | Starnum disclosure | Official source |
|---|---|---|---|
| OpenAI | Model documentation, latest model notes, safety best practices, and data controls. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://platform.openai.com/docs/models |
| Anthropic | Claude model documentation, system/model cards, Responsible Scaling, and safety policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://docs.anthropic.com/en/docs/about-claude/models |
| Google Gemini | Gemini API model documentation, safety settings, data governance, and platform policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://ai.google.dev/gemini-api/docs/models |